Time-varying power spectra and coherences of non-stationary typhoon winds

被引:30
|
作者
Huang, Zifeng [1 ]
Xu, You-Lin [1 ]
Tao, Tianyou [2 ]
Zhan, Sheng [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China
[2] Southeast Univ, Sch Civil Engn, Nanjing, Peoples R China
关键词
Typhoon winds; S-transform-based method; Time-varying wind spectrum; Time-varying wind coherence; Typhoon Hato; SUTONG BRIDGE; EVOLUTIONARY SPECTRA; ESTIMATION SUBJECT; DECOMPOSITION; SIMULATION; SPEED;
D O I
10.1016/j.jweia.2020.104115
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Typhoon winds near its external eye wall are strongly non-stationary and disastrous, requiring a deep understanding. This paper first presents an S-transform-based method for obtaining the time-varying power spectra and coherences of a multivariate non-stationary process. The accuracy of the proposed S-transform-based method is examined through a comparison with currently-used two methods. The analytical expressions of time-varying power spectra and coherences of non-stationary typhoon winds are then proposed by introducing time-varying parameters into the stationary Von Karman wind spectra and the stationary Krenk wind coherence functions respectively. Finally, the S-transform-based method is applied to the wind data recorded by the multiple anemometers installed in the Stonecutters Bridge in Hong Kong during Typhoon Hato, and the resulting time-varying wind spectra and coherences are fitted by the analytical expressions of time-varying Von Karman wind spectra and Krenk wind coherence functions respectively. The results show that the typhoon winds recorded during Typhoon Hato are clearly non-stationary and that the time-varying Von Karman wind spectra and Krenk wind coherence functions could well fit the wind data recorded during Typhoon Hato.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] A similarity-based approach to time-varying coefficient non-stationary autoregression
    Lieberman, Offer
    JOURNAL OF TIME SERIES ANALYSIS, 2012, 33 (03) : 484 - 502
  • [22] A wavelet-based time-varying autoregressive model for non-stationary and irregular time series
    Salcedo, G. E.
    Porto, R. F.
    Roa, S. Y.
    Momo, F. R.
    JOURNAL OF APPLIED STATISTICS, 2012, 39 (11) : 2313 - 2325
  • [23] Modelling non-stationary signals by time-dependent AR process with time-varying gain
    Mukhopadhyay, S
    Sircar, P
    IETE JOURNAL OF RESEARCH, 1997, 43 (05) : 351 - 358
  • [24] Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments
    Li, Changzhen
    Chen, Wei
    Pei, Zhonghui
    Chang, Fuxing
    Yu, Junyi
    Luo, Fan
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [25] Adaptive channel estimation for OFDM systems in time-varying non-stationary wireless channels
    Zeng, Jianqiang
    Minn, Hlaing
    2007 IEEE SARNOFF SYMPOSIUM, 2007, : 304 - 308
  • [26] Polynomial time-frequency distributions and time-varying higher-order spectra: A review of performance for non-stationary signal analysis
    Boashash, B
    Ristic, B
    ISSPA 96 - FOURTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, PROCEEDINGS, VOLS 1 AND 2, 1996, : 650 - 653
  • [27] Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments
    Changzhen Li
    Wei Chen
    Zhonghui Pei
    Fuxing Chang
    Junyi Yu
    Fan Luo
    Scientific Reports, 12
  • [28] Extracting non-stationary signal under strong noise background: Time-varying system analysis
    Shan, Zhen
    Wang, Zhongqiu
    Yang, Jianhua
    Zhou, Dengji
    Liu, Houguang
    JOURNAL OF VIBRATION AND CONTROL, 2023, 29 (17-18) : 4036 - 4045
  • [29] Utilizing higher moments to detect time-varying target in radar echo with non-stationary background
    Gui, Renzhou
    INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES, 2016, 8 (02) : 215 - 219
  • [30] Time-Varying Functional Principal Components for Non-Stationary EpCO2 in Freshwater Systems
    Elayouty, Amira
    Scott, Marian
    Miller, Claire
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2022, 27 (03) : 506 - 522